Construction Of Composite Indicators Research Articles

Setting S&T perspective priorities in an era of complexity and uncertainty: a case for megacities

PurposeThe purpose of this study is to create a methodological approach for identifying priority areas for science and technology (S&T) development and its empirical application within the city of Moscow. This research uncovers a wide range of multicultural and multidisciplinary global trends that will affect the development of major cities in an era of complexity and uncertainty, including the inherent complexity of urban contexts, demographic and socioeconomic trends, as well as scientific and ecological factors.Design/methodology/approachThe methodological approach is based on classic foresight instruments. Its novelty lays in the blending of qualitative and quantitative methods specially selected as the most appropriate for the identification of S&T areas in an era of complexity and uncertainty, including horizon scanning, bibliometric analysis, expert surveys and the construction of composite indexes with respect to the scope and resources of the research and the selected object for empirical application – Moscow, which is one of the world’s largest megacities. The analysis was performed for the period of 2009–2018 and expert procedures took place in 2019.FindingsAs a result, 25 global trends were identified, evaluated and discussed over the course of an expert survey and subsequent expert events. Ten priority areas of S&T development were determined, including 62 technological sub-areas within them and the most important market niches for all identified technological sub-areas, which could be useful for the world’s megacities. The results of this study are illustrated using the construction sector. Based on the conducted research and results, a list of recommendations on S&T policy measures and instruments were suggested, including the creation of the Moscow Innovation Cluster, which by the end of 2023 contained more than 6,000 projects and initiatives, selected using the findings of this investigation.Originality/valueThis research contributes to the existing literature and research agenda of setting priorities for S&T development and shows how it can be done for a megacity. The blended foresight methodology that was created within the study satisfies the criteria of scientific originality, is repeatable for any interested researcher, is applicable to any other city in the world and demonstrates its high efficiency in empirical application. It could be used for creating new agenda items in S&T policy, setting S&T priorities for a megacity and integrating the results into decision-making processes. This study provides recommendations on the further implementation of the designed methodology and results into a policymaking system. Moreover, the example of the Moscow Innovation Cluster, which was created based on the results of our research, demonstrates these recommendations’ practical significance in real life, which is quite valuable. The limitation of this study is that it is not devoted to urban planning issues directly or the promotion of R&D areas; it is about setting promising S&T priorities in an era of complexity and uncertainty for megacities.

Augmenting business statistics information by combining traditional data with textual data: a composite indicator approach

Combining traditional and digital trace data is an emerging trend in statistics. In this respect, new data sources represent the basis for multi-purpose extraction of different statistical indicators, which contribute to augmenting the statistical information, for feeding smart statistics. The production of business statistics can benefit from the use of unstructured data, especially to study novel aspects which are not covered by traditional data sources. This paper proposes a methodological general framework for augmenting information by combining data, both structured and non structured. The statistical challenges of using unstructured data and their integration with traditional data are discussed. The methodological general framework is applied to the construction of smart composite indicators using social media data and their metadata. An empirical exercise illustrates how to apply the methodology in practice.

Proposal to measure tourism development within a destination and segment its different territories

Tourism offers a means of promoting a local development strategy capable of harnessing the resources available within a territory. In order to do this, we must know the potential for tourism development in each territory, as well as the factors that would condition it. In this article, a methodology (based on the design of a system of indicators, the construction of composite indexes, and segmentation by means of cluster analysis) is proposed to measure tourism development within an emerging country and segment its different territories. The case study chosen to validate this methodology is Ecuador, a country with interesting tourist potential, where individual cities have very different levels of tourism development. The results highlight the factors that drive or constrain the degree of tourism development presented by the cities analysed, thereby facilitating decision-making for major stakeholders in each of them.

Assessing the robustness of composite indicators: the case of the Global Innovation Index

This research paper introduces a methodology to assess the robustness of the Global Innovation Index (GII), by comparing the rankings provided in it with those achieved using alternative data-driven methodologies such as data envelopment analysis (DEA) and principal component analysis (PCA). With it, the paper aims to reduce the level of subjectivity in the construction of composite indicators regarding weight generation and indicator aggregation. The paper relies on PCA as a weighting-aggregation scheme to reproduce the 21 sub-pillars of the GII before the application of DEA to calculate the relative efficiency score for every country. By using the PCA-DEA model, a final ranking is produced for all countries. The random forests (RF) classification is used examine the robustness of the new rank. The comparison between the new rank and that of the GII suggests that the countries positioned at the top or the bottom of the GII rank are less sensitive toward the modification than those in the middle of the GII, the rank of which is not robust against the modification of the construction method. The PCA-DEA model introduced in this paper provides policymakers with an effective tool to monitor the performance of national innovation policies from the perspective of their relative efficiency. Ultimately, the contribution made in this paper could be instrumental to enhance the effectiveness and the efficiency of the practice of innovation management at the national level.

Measurement of community resilience using the Baseline Resilience Indicator for Communities (BRIC) framework: A systematic review

Community resilience is an important concept in disaster prevention policy, as it is hypothesised to mitigate the impacts of ‘shocks’ and facilitate adaptation to reduce the negative impact of future adverse events. The field of community resilience measurement has developed in recent years, with composite indices emerging as a popular methodology. An initial scoping review identified the Baseline Resilience Index for Communities (BRIC) as the most replicated quantitative method of measuring community resilience. A systematic review was undertaken to assess how the BRIC methodology has been used to measure community resilience. The review describes the geographical locations, types of communities and ‘shocks’ in which the BRIC has been applied. The methodology quality was assessed and scored using the OECD framework for composite indicator development. The review identified 32 relevant papers. There was variation in the number of resilience sub-domains, indicators, definition of communities and the type of ‘shock’. The median quality assessment score for studies was 60% (IQR 40–70%), no papers completed all the recommended steps. Quality assessment identified strengths in data selection, and weakness in sensitivity analyses and handling of missing data. Measurement of community resilience is an emergent field and there is a lack of methodological consensus. Some variation in how the BRIC model has been applied is warranted as it is necessary to make adaptations to the local context. Improvement in the methodological quality of composite index construction could lead to wider use of these tools in disaster risk reduction.

Building Composite Indicators With Unweighted-TOPSIS

Composite indicators have been widely used in a large number of fields, including innovation and entrepreneurship as a useful tool for conveying summary information about overall performance in a relatively simple way. The construction of composite indicators implies several stages concerning collection of data, selection of criteria and individual indicators, normalization and weighting of criteria and indicators, aggregation, and comparison of overall performance of the alternatives or options. This article aims at contributing to the construction of synthetic indicators by showing with a real example, how the proposed methodology can overcome the problem of the establisment of the decision criteria relative importance. Determination of weighting schemes can be a controversial question for decision makers in those contexts where subjective weights are used. The method proposed in this article allows the ranking of a set of alternatives without the establishment of a priori criteria weights. In our proposal, based on TOPSIS, weights are unknown variables contributing to the optimization of the relative proximity of each alternative to an ideal solution.

The Hybrid Factor Analysis and Analytic Network Process (F'ANP) model modified: Assessing community social resilience in Tehran metropolis

The F'ANP model was proposed to overcome one of the inherent drawbacks of the Analytic Network Process (ANP): the subjective judgements of decision makers in the construction of pairwise comparison matrices. The model introduced construction of data-driven matrices instead of the ANP subjective judgements. The model is now modified to overcome the second main drawback of the ANP model: the construction of great number of pairwise comparison matrices. Also, the F'ANP model utilizes the inherent features of Factor Analysis (FA) to streamline the construction of the ANP network model systematically. The proposed model could be used to weigh the indicators that depict a multi-dimensional phenomenon and also be used for composite index construction. It is applied to assess the social resilience to disasters at the neighborhoods level in Tehran. The results show that four correlated factors represent community social resilience: social structure, social equity, social values, and social capital. The composite social resilience index shows that the northern parts of the city have higher levels of social resilience, whereas the southern parts of the city exhibit lower social resilience capacity towards disasters. The model's application in Tehran shows that the modified F'ANP model is a concise and robust model to use.

Goal-based participatory weighting scheme: balancing objectivity and subjectivity in the construction of composite indicators

Goal-based participatory weighting scheme: balancing objectivity and subjectivity in the construction of composite indicators

Potential improvements approach in composite indicators construction: The Multi-directional Benefit of the Doubt model

The aim of the present paper is to propose an innovative method to construct composite indicators, starting from an attractive frontier approach called Multi-directional Efficiency Analysis (MEA), and making it suitable to aggregate simple indicators. The main advantage of this method is the separation of the benchmark selection issue from that of efficiency measurement. The novel method, called Multi-directional Benefit of the Doubt, combines the MEA with the Benefit of the Doubt approach adding some valuable properties to the obtained composite indicator. The proposed method is tested on simulated data for illustrative purposes, and then, an empirical application on OECD ”Better life index” is provided. Results suggest that the method could be a good tool for local policy makers alongside the OECD BLI to investigate and improve some non-performing well-being dimensions.

A Composite Indicator Methodology to Rank National Olympic Committees

This paper proposes a composite indicator methodology to rank National Olympic Committees (NOCs) by considering only the medals won at both Summer and Winter Olympic games. The lexicographic and sum ranking systems at both Summer and Winter Olympics are comprehensively investigated to create the preliminary decision matrix for constructing composite indicators. Then we investigate the preferential difference and preferential priority for better reflecting the preference structures of the sub-indicators, and thereby obtain a revised decision matrix. The SMAA-2 is employed to implement the composite indicator construction based upon the revised decision matrix. An empirical study is performed to demonstrate the applicability of our methodology, using the data set that is composed of 28 NOCs who have participated in both the Rio 2016 Summer Olympic Games and the PyeongChang 2018 Winter Olympics. The Spearman’s rank correlation coefficient and the satisfaction index are calculated to justify the merits of the proposed methodology.

Interpretability of Composite Indicators Based on Principal Components

Principal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and possible conflicts with the theoretical framework when the signs in the loading are not aligned with the expected direction of impact. In this study, we propose an adjustment in the construction of principal component approaches to avoid these problems. The effectiveness of the proposed approach is illustrated in defining the Food and Agriculture Organization of the United Nations’ Resilience Capacity Index, which is used to measure household-level resilience to food insecurity. We conclude that the robustness gain of using the new method improves the reliability of the composite indicator.

Avaliação da validade do Global Entrepreneurship Index

Purpose: our objective is to assess whether the Global Entrepreneurship Index (GEI) is a valid methodology for measuring the national systems of entrepreneurship. Theoretical framework: we use the structural equation modeling (SEM) as a theoretical lens to evaluate the Global Entrepreneurship Index. Design/methodology/approach: to achieve the research objective, we perform structural equation modeling to measure the formative model of GEI, using the SmartPLS 3.0 software, in order to verify the convergent validity of the constructs; the collinearity of indicators and their relevance for measuring entrepreneurship. Findings: the results obtained show that the sub-indices and indicators are adequate to measure entrepreneurship. However, we identified that there is a need to replace the micro-level components of the opportunity perception and networking indicators which provide distorted results of what is understood as productive or high impact entrepreneurship. Research, practical and social implications: the main contributions of our study are directed to the indicators’ developers. We suggest the use of components that capture aspects associated with high-impact or technology-based entrepreneurship instead of any attempt to create a new business, as our results indicate that generic components provide short-sighted results on the state of entrepreneurship, hampering comparative studies at the country-level. Originality/value: although the GEI is based on the OECD recommendations for the construction of composite indicators (indices), we have not identified any studies (not even performed by the GEI developers) on the validity of the index as a methodology for measuring entrepreneurship.

Effect of social capital on youth self-employment in Holeta town, Ethiopia

PurposeSocial resources have gained considerable interest from scholars for their effect on youth's labor market outcomes. The available evidence indicates that social capital is a crucial factor in determining youth's labor force status. This paper examines the effect of social capital components on youth self-employment in Holeta town, Ethiopia.Design/methodology/approachUsing youth survey data, principal component analysis (PCA) was conducted on social capital components datasets. This provided the construction of composite indicators for measuring social capital component, which are robust and exhibited construct validity. Following this, the effect of social capital component on youth self-employment was examined using Logit model.FindingsThe results show that institutional trust positively affects the likelihood of young people's decision to be self-employed.Practical implicationsThe finding reveals a need to improve the transparency of the institutions' resource delivery, the competency of the institutions' workforce, communication, and information sharing of the institutions with the youth.Originality/valueLooking at different social capital components simultaneously, the paper provides insight into the wide range of effects of social capital on youth self-employment.

GUIDELINE FOR CONSTRUCTING COMPOSITE INDICATORS THAT MEASURE CIRCULAR ECONOMY PERFORMANCE

Composite indicators are modern tools for evaluating, comparing, and measuring the performance of multidimensional concepts such as sustainable development, environmental protection, or circular economy among countries, regions, or cities. The core of composite indicators includes the mathematical relation of individual indicators pertaining to different dimensions of the observed concept. Importantly, composite indicators are constructed by means of various mathematical models, primarily multiple-criteria decision analysis, which allows the selection of adequate individual indicators within a composite indicator. This is followed by the selection of a suitable aggregation model and a careful selection of a system for assigning individual indicator weights. Subjectivity during decision-making should be avoided as much as possible, as it could lead to an imprecisely formed indicator. In addition, this requires a proper previous definition of indicator robustness, which explains the level of coverage of a specific concept, as well as potential restrictions that need to be listed in indicator notes. This paper discusses the key features of measurement using indicators and the application of composite indicators in circular economy. Guidelines are also provided for proper definitions and construction of composite indicators that measure circular economy performance, all for the purpose of improving the system for circular economy performance measurement.

Composite Index for Comprehensive Assessment of Power System Transient Stability

This paper proposes a Principal Component Analysis (PCA)-based method to construct composite indices to rapidly assess power system transient stability. By assigning appropriate weights to a few selected sub-indicators, a composite index can be constructed to guarantee a better coverage of worst-case stability scenarios of the power system. This paper provides an automatic method to construct a composite index with four sub-indicators calculated by the integral of acceleration, speed deviation and rotor angle of generators over a certain integration period using PCA. The effectiveness of the sub-indicators is validated first, followed by a comprehensive example of process of composite index construction, and the assessment of the influence of the effect of integration period on the performance of the composite index. High accuracy of classification has been demonstrated using composite index with appropriate settings of the integration periods of the sub-indicators.

Constructing fuzzy composite indicators to support water policy entrepreneurship

Composite indicators play a very useful role in supporting international policy entrepreneurship, helping to inform decision-making processes and to address conflicts between countries. In this paper we present a Fuzzy Data Envelopment Analysis (Fuzzy-DEA) model to construct composite indicators based on time series under the Pressure-State-Response (PSR) approach in order to evaluate water security in 10 European water security hotspots. First, we identified nine indicators within the PSR framework. Second, we aggregated the indicators in each PSR set using a Fuzzy-DEA model. Finally, we compared the PSR results under pessimistic, optimistic and neutral scenarios. Results show that, overall, Bulgaria achieved the best pressure, state and response performance. In terms of responses, Spain registered the best performance after Bulgaria, despite the fact that it suffers from the highest pressures of all the analyzed countries. Estonia achieved the best state performance in all three scenarios, but worse results in responses than Bulgaria and Spain. Cyprus and Belgium registered the highest variability for pressures, Spain for state and Portugal for responses.The model was able to pinpoint the problems that political strategies and urgent actions should seek to tackle, while providing information regarding the variability of the indicators over time.

Dealing with weighting scheme in composite indicators: An unsupervised distance-machine learning proposal for quantitative data

There is increasing interest in the construction of composite indicators to benchmark units. However, the mathematical approach on which the most commonly used techniques are based does not allow benchmarking in a reliable way. Additionally, the determination of the weighting scheme in the composite indicators remains one of the most troubling issues. Using the vector space formed by all the observations, we propose a new method for building composite indicators: a distance or metric that considers the concept of proximity among units. This approach enables comparisons between the units being studied, which are always quantitative. To this end, we take the P2 Distance method of Pena Trapero as a starting point and improve its limitations. The proposed methodology eliminates the linear dependence on the model and seeks functional relationships that enable constructing the most efficient model. This approach reduces researcher subjectivity by assigning the weighting scheme with unsupervised machine learning techniques. Monte Carlo simulations confirm that the proposed methodology is robust.

빅데이터 DEA를 위한 기계학습의 적용

Data envelopment analysis (DEA) is a tool for identifying best-practices when multiple performance metrics or measures are present for decision-making units (DMUs). As big data issue becomes an important area of supply chain and operations management, DEA is evolving into a data-oriented data science tool for benchmarking, performance evaluation, composite index construction and others. As the number of DMUs increases, the running-time to solve the standard DEA model sharply rises. Such situations are appearing more frequently in the era of big data. This issue could be an important challenge particularly when real-time data stream in at extremely high rates and the DEA analysis needs to be performed very quickly. Therefore, there exist practical needs for developing an efficient way of solving large-scale DEA problems. In this paper, we propose a practical approach for speeding up the DEA efficiency estimation process based on machine learning. In this approach, a sample of DMUs is selected from the population as a training data set, based on which a machine is trained to predict the efficiency scores of unselected or newly streamed-in DMUs. We also suggest a data augmentation technique to enhance the learning process under severe data class imbalance. The superior performance of the proposed approach over the conventional one in terms of efficiency prediction power as well as model computation time is shown through a series of computational experiments using randomly generated data.

Analysis of Development Index of Land Border Area through Composite Index Construction

The land border area in Indonesia is a strategic area from a social, economic, political, cultural, and security perspective. The arrangement and development of the border area as a strategic area are needed to synchronize the policies of the central and regional governments. Even though it has several problems, currently, the development of the border area has become one of the development priorities by the government. The Development Index of Land Border Area was built to analyze development dynamics in border areas during 2018 - 2019 using Linear Aggregation Model (OECD 2008). It is used as a reference in program preparation and evaluation of central and regional government policies related to border development and management. This research was conducted in twenty-one districts in the land border area of Indonesia, covering five provinces, namely West Kalimantan (Kalimantan Barat), East Kalimantan, North Kalimantan, East Nusa Tenggara, and Papua. The dimensions used in this calculation are economic, social, infrastructure, communication technology, and the environment, as well as eleven sub-dimensions and twenty-nine variables. All observation areas have economic resources in the agricultural sector, but some areas are also tourist areas. Over ten years, the development index for Indonesia’s land border areas was quite volatile but still ranged from 10- 20. During 2010 - 2019, Berau (East Kalimantan) achieved the highest development composite index of 25.4, and the lowest was Sabu Raijua (East Nusa Tenggara) achieved the lowest composite index of 19.8.

Composite Index Construction with Expert Opinion

Composite index is a powerful and popularly used tool in providing an overall measure of a subject by summarizing a group of measurements (component indices) of different aspects of the subject. It is widely used in economics, finance, policy evaluation, performance ranking, and many other fields. Effective construction of a composite index has been studied extensively. The most widely used approach is to use a linear combination of the component indices, where the combination weights are determined by optimizing an objective function. To maximize the overall variation of the resulting composite index, the combination weights can be obtained through principal component analysis. In this article, we propose to incorporate expert opinions into the construction of the composite index. It is noted that expert opinion often provides useful information in assessing which of the component indices are more important for the overall measure of the subject. We consider the case that a group of experts have been consulted, each providing a set of importance scores for the component indices, along with a set of confidence scores which reflects the expert’s own confidence in his/her assessment. In addition, the constructor of the composite index can also provide an assessment of the expertise level of each expert. We use linear combinations to construct the composite index, where the combination weights are determined by maximizing the sum of resulting composite index variation and the negative weighted sum of squares of deviation between the combination weights used and the experts’ scores. A data-driven approach is used to find the optimal balance between the two sources of information. Theoretical properties of the procedure are investigated. Simulation examples and an economic application on constructing science and technology development index is carried out to illustrate the proposed method.